code: def inception2BN(conf):
      layer = {"layer": Inception, "partitions": []}
      partitions = layer["partitions"]
  
      if conf[0][0]:
          partitions.append(
              (
                  {
                      "layer": ConvLayer,
                      "common": conv1,
                      "K": conf[0][0],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
              )
          )
  
      partitions.extend(
          (
              (
                  {
                      "layer": ConvLayer,
                      "common": conv1,
                      "K": conf[1][0],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
                  {
                      "layer": ConvLayer,
                      "common": conv3,
                      "K": conf[1][1],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
              ),
              (
                  {
                      "layer": ConvLayer,
                      "common": conv1,
                      "K": conf[2][0],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
                  {
                      "layer": ConvLayer,
                      "common": conv3,
                      "K": conf[2][1],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
                  {
                      "layer": ConvLayer,
                      "common": conv3,
                      "K": conf[2][1],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
              ),
          )
      )
      if conf[3][1]:
          partitions.append(
              (
                  {
                      "layer": PoolLayer,
                      "common": pool3s1p1,
                      "op": conf[3][0],
                  },
                  {
                      "layer": ConvLayer,
                      "common": conv1,
                      "K": conf[3][1],
                      "bsum": True,
                  },
                  {"layer": BatchNorm, "relu": True, "bsum": True},
              )
          )
      else:
          partitions.append(
              (
                  {
                      "layer": PoolLayer,
                      "common": pool3s1p1,
                      "op": conf[3][0],
                  },
              )
          )
      return layer
  
file_path: neon/neon/backends/convnet-benchmarks.py
imports: from __future__ import division
  import numpy
  import pycuda.driver
  from neon import logger
  from neon.backends.nervanagpu import NervanaGPU
  from neon.backends.layer_gpu import Layer, DataLayer, ConvLayer, PoolLayer, FullLayer, Inception, BatchNorm
label_desc: Use image node_id 4 for calling a global function with example usage: inception2BN(conf) and returns: layer